Overview

Dataset statistics

Number of variables17
Number of observations387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.5 KiB
Average record size in memory136.3 B

Variable types

Numeric12
Categorical5

Warnings

RSI_14 is highly correlated with Z_30High correlation
Z_30 is highly correlated with RSI_14High correlation
Z_30 has unique values Unique
ratio_M50M180 has unique values Unique
ratio_M5M20 has unique values Unique
ratio_M20M50 has unique values Unique
ratio_MACDh_12_26_9 has unique values Unique
obv_pct_delta has unique values Unique
tr_pct_delta has 8 (2.1%) zeros Zeros

Reproduction

Analysis started2021-04-10 02:51:18.385681
Analysis finished2021-04-10 02:51:57.915295
Duration39.53 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

close
Real number (ℝ≥0)

Distinct297
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9763049
Minimum3.119999886
Maximum13.96000004
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:51:58.149203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.119999886
5-th percentile3.950000048
Q15.36500001
median6.809999943
Q311.55500031
95-th percentile13.1869998
Maximum13.96000004
Range10.84000015
Interquartile range (IQR)6.190000296

Descriptive statistics

Standard deviation3.259429208
Coefficient of variation (CV)0.4086389937
Kurtosis-1.433638972
Mean7.9763049
Median Absolute Deviation (MAD)2.539999962
Skewness0.3159206578
Sum3086.829996
Variance10.62387876
MonotocityNot monotonic
2021-04-09T21:51:58.434223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.9499998094
 
1.0%
5.5100002294
 
1.0%
4.1300001144
 
1.0%
5.4899997714
 
1.0%
4.2300000193
 
0.8%
6.1199998863
 
0.8%
11.300000193
 
0.8%
12.199999813
 
0.8%
5.9800000193
 
0.8%
11.939999583
 
0.8%
Other values (287)353
91.2%
ValueCountFrequency (%)
3.1199998861
0.3%
3.240000011
0.3%
3.2899999621
0.3%
3.3499999051
0.3%
3.4500000481
0.3%
ValueCountFrequency (%)
13.960000041
0.3%
13.789999961
0.3%
13.751
0.3%
13.689999581
0.3%
13.680000311
0.3%

RSI_14
Real number (ℝ≥0)

HIGH CORRELATION

Distinct385
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.61926414
Minimum9.134719431
Maximum79.47982393
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:51:58.717244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9.134719431
5-th percentile24.01144696
Q141.66736517
median50.20145171
Q359.38336704
95-th percentile70.62333135
Maximum79.47982393
Range70.3451045
Interquartile range (IQR)17.71600187

Descriptive statistics

Standard deviation13.93363017
Coefficient of variation (CV)0.2808108991
Kurtosis-0.2771180928
Mean49.61926414
Median Absolute Deviation (MAD)8.961706264
Skewness-0.2572057777
Sum19202.65522
Variance194.1460498
MonotocityNot monotonic
2021-04-09T21:51:59.013268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.292947392
 
0.5%
44.701907112
 
0.5%
53.186997711
 
0.3%
22.772602821
 
0.3%
40.774122471
 
0.3%
44.105738181
 
0.3%
70.987426991
 
0.3%
27.852729351
 
0.3%
48.024060511
 
0.3%
52.609901071
 
0.3%
Other values (375)375
96.9%
ValueCountFrequency (%)
9.1347194311
0.3%
14.966066271
0.3%
15.963643361
0.3%
16.790657051
0.3%
17.175153061
0.3%
ValueCountFrequency (%)
79.479823931
0.3%
78.970851761
0.3%
77.95000971
0.3%
77.376235831
0.3%
77.347545021
0.3%

INC_2
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0
202 
1
185 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
0202
52.2%
1185
47.8%
2021-04-09T21:51:59.634316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-09T21:51:59.907338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0202
52.2%
1185
47.8%

Most occurring characters

ValueCountFrequency (%)
0202
52.2%
1185
47.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number387
100.0%

Most frequent character per category

ValueCountFrequency (%)
0202
52.2%
1185
47.8%

Most occurring scripts

ValueCountFrequency (%)
Common387
100.0%

Most frequent character per script

ValueCountFrequency (%)
0202
52.2%
1185
47.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII387
100.0%

Most frequent character per block

ValueCountFrequency (%)
0202
52.2%
1185
47.8%

ROC_2
Real number (ℝ)

Distinct384
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1934257979
Minimum-53.46153813
Maximum35.31300609
Zeros3
Zeros (%)0.8%
Memory size3.1 KiB
2021-04-09T21:52:00.199359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-53.46153813
5-th percentile-8.003109923
Q1-3.29635401
median-0.1972431665
Q33.50430149
95-th percentile11.46730461
Maximum35.31300609
Range88.77454422
Interquartile range (IQR)6.8006555

Descriptive statistics

Standard deviation7.14487615
Coefficient of variation (CV)36.93858951
Kurtosis11.17601157
Mean0.1934257979
Median Absolute Deviation (MAD)3.385645879
Skewness-0.7600591474
Sum74.8557838
Variance51.0492552
MonotocityNot monotonic
2021-04-09T21:52:00.652395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.8%
-0.36166329422
 
0.5%
18.648023261
 
0.3%
2.5641036711
 
0.3%
-1.8992530861
 
0.3%
15.178569981
 
0.3%
-4.5302009931
 
0.3%
-6.7915682241
 
0.3%
4.8962667491
 
0.3%
-3.0952296071
 
0.3%
Other values (374)374
96.6%
ValueCountFrequency (%)
-53.461538131
0.3%
-35.578328781
0.3%
-20.088306451
0.3%
-18.276757791
0.3%
-16.494847581
0.3%
ValueCountFrequency (%)
35.313006091
0.3%
23.025216911
0.3%
22.760291311
0.3%
22.645293911
0.3%
20.618557111
0.3%

PSL_3
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
66.66666666666667
157 
33.333333333333336
151 
100.0
41 
0.0
38 

Length

Max length18
Median length17
Mean length14.74418605
Min length3

Characters and Unicode

Total characters5706
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33.333333333333336
2nd row66.66666666666667
3rd row0.0
4th row100.0
5th row33.333333333333336
ValueCountFrequency (%)
66.66666666666667157
40.6%
33.333333333333336151
39.0%
100.041
 
10.6%
0.038
 
9.8%
2021-04-09T21:52:01.381451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-09T21:52:01.624468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
66.66666666666667157
40.6%
33.333333333333336151
39.0%
100.041
 
10.6%
0.038
 
9.8%

Most occurring characters

ValueCountFrequency (%)
62506
43.9%
32416
42.3%
.387
 
6.8%
0199
 
3.5%
7157
 
2.8%
141
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5319
93.2%
Other Punctuation387
 
6.8%

Most frequent character per category

ValueCountFrequency (%)
62506
47.1%
32416
45.4%
0199
 
3.7%
7157
 
3.0%
141
 
0.8%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5706
100.0%

Most frequent character per script

ValueCountFrequency (%)
62506
43.9%
32416
42.3%
.387
 
6.8%
0199
 
3.5%
7157
 
2.8%
141
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5706
100.0%

Most frequent character per block

ValueCountFrequency (%)
62506
43.9%
32416
42.3%
.387
 
6.8%
0199
 
3.5%
7157
 
2.8%
141
 
0.7%

CDL_DOJI_3_0.1
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0.0
342 
1.0
45 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1161
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.0342
88.4%
1.045
 
11.6%
2021-04-09T21:52:02.149509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-09T21:52:02.363526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0342
88.4%
1.045
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0729
62.8%
.387
33.3%
145
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number774
66.7%
Other Punctuation387
33.3%

Most frequent character per category

ValueCountFrequency (%)
0729
94.2%
145
 
5.8%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1161
100.0%

Most frequent character per script

ValueCountFrequency (%)
0729
62.8%
.387
33.3%
145
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161
100.0%

Most frequent character per block

ValueCountFrequency (%)
0729
62.8%
.387
33.3%
145
 
3.9%

TRUERANGE_1
Real number (ℝ≥0)

Distinct164
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4353488366
Minimum0.1199998856
Maximum3.50999999
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:02.607543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1199998856
5-th percentile0.1730000019
Q10.279999733
median0.3699998856
Q30.5199999809
95-th percentile0.9270002365
Maximum3.50999999
Range3.390000105
Interquartile range (IQR)0.240000248

Descriptive statistics

Standard deviation0.2753378828
Coefficient of variation (CV)0.6324534709
Kurtosis41.29600439
Mean0.4353488366
Median Absolute Deviation (MAD)0.1100001335
Skewness4.587895211
Sum168.4799998
Variance0.07581094969
MonotocityNot monotonic
2021-04-09T21:52:02.897565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2515
 
3.9%
0.369999885611
 
2.8%
0.340000152611
 
2.8%
0.329999923711
 
2.8%
0.289999961910
 
2.6%
0.38000011447
 
1.8%
0.36000013357
 
1.8%
0.26999998096
 
1.6%
0.30000019076
 
1.6%
0.2799997336
 
1.6%
Other values (154)297
76.7%
ValueCountFrequency (%)
0.11999988561
0.3%
0.12999963761
0.3%
0.13999986652
0.5%
0.14000034331
0.3%
0.14999961852
0.5%
ValueCountFrequency (%)
3.509999991
0.3%
1.6500005721
0.3%
1.4300003051
0.3%
1.3499999051
0.3%
1.2299995421
0.3%

Z_30
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1236049437
Minimum-3.188764919
Maximum4.153555278
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:03.204590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.188764919
5-th percentile-2.075517898
Q1-1.021445216
median-0.006576751663
Q31.319187509
95-th percentile2.281206163
Maximum4.153555278
Range7.342320197
Interquartile range (IQR)2.340632725

Descriptive statistics

Standard deviation1.426586171
Coefficient of variation (CV)11.54149768
Kurtosis-0.7710374298
Mean0.1236049437
Median Absolute Deviation (MAD)1.141797759
Skewness0.1082263451
Sum47.83511321
Variance2.035148103
MonotocityNot monotonic
2021-04-09T21:52:03.496612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.601849231
 
0.3%
-0.73933358531
 
0.3%
2.227650941
 
0.3%
-0.50782478641
 
0.3%
1.7113558521
 
0.3%
1.7256510591
 
0.3%
-2.031482031
 
0.3%
-1.7530641421
 
0.3%
-2.4082822361
 
0.3%
1.3194767491
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-3.1887649191
0.3%
-2.8399971371
0.3%
-2.8242857461
0.3%
-2.7836534941
0.3%
-2.499115541
0.3%
ValueCountFrequency (%)
4.1535552781
0.3%
3.9826359791
0.3%
3.4548709851
0.3%
3.1521800411
0.3%
2.952724561
0.3%

ratio_M50M180
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.02609194
Minimum-16.74797861
Maximum22.39522982
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:03.788634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-16.74797861
5-th percentile0.759917066
Q10.9589723847
median0.9815822107
Q31.04348095
95-th percentile1.328490957
Maximum22.39522982
Range39.14320843
Interquartile range (IQR)0.08450856558

Descriptive statistics

Standard deviation1.515975958
Coefficient of variation (CV)1.477427021
Kurtosis153.3959578
Mean1.02609194
Median Absolute Deviation (MAD)0.03686871047
Skewness3.539600884
Sum397.0975807
Variance2.298183104
MonotocityNot monotonic
2021-04-09T21:52:04.071656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1845648381
 
0.3%
1.0454612881
 
0.3%
0.89078057761
 
0.3%
0.97218669521
 
0.3%
0.96721392181
 
0.3%
1.0235374561
 
0.3%
0.94342466931
 
0.3%
0.95674996271
 
0.3%
1.0784830751
 
0.3%
0.96812522631
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-16.747978611
0.3%
-2.1619750731
0.3%
-1.7333340451
0.3%
-0.12533994591
0.3%
-0.090359621351
0.3%
ValueCountFrequency (%)
22.395229821
0.3%
9.6227767521
0.3%
2.7797719881
0.3%
2.6134029351
0.3%
2.4189053591
0.3%

ratio_M5M20
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4345999566
Minimum-124.6648044
Maximum52.38880441
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:04.380681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-124.6648044
5-th percentile-1.398992109
Q10.6017050804
median0.9178342898
Q31.271961452
95-th percentile2.660546691
Maximum52.38880441
Range177.0536088
Interquartile range (IQR)0.6702563719

Descriptive statistics

Standard deviation8.570820404
Coefficient of variation (CV)19.72117179
Kurtosis151.488364
Mean0.4345999566
Median Absolute Deviation (MAD)0.3416220085
Skewness-10.49245745
Sum168.1901832
Variance73.4589624
MonotocityNot monotonic
2021-04-09T21:52:04.818712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9748968021
 
0.3%
0.97205165741
 
0.3%
3.6090853761
 
0.3%
0.4968474621
 
0.3%
1.0424746521
 
0.3%
1.3513697311
 
0.3%
1.5349306971
 
0.3%
0.92231705531
 
0.3%
0.5302351561
 
0.3%
1.6508592711
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-124.66480441
0.3%
-89.125092391
0.3%
-18.999961211
0.3%
-10.500003971
0.3%
-9.4651209731
0.3%
ValueCountFrequency (%)
52.388804411
0.3%
18.727091451
0.3%
12.249988081
0.3%
11.10907681
0.3%
8.487181181
0.3%

ratio_M20M50
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.044677755
Minimum-30.19174175
Maximum56.79845366
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:05.104932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-30.19174175
5-th percentile0.3950546633
Q10.8804531867
median0.9865125216
Q31.140026046
95-th percentile1.820431956
Maximum56.79845366
Range86.99019541
Interquartile range (IQR)0.2595728597

Descriptive statistics

Standard deviation3.501976297
Coefficient of variation (CV)3.352207206
Kurtosis186.4943012
Mean1.044677755
Median Absolute Deviation (MAD)0.1291895292
Skewness8.080002696
Sum404.290291
Variance12.26383799
MonotocityNot monotonic
2021-04-09T21:52:05.393956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1668918161
 
0.3%
0.88416575131
 
0.3%
0.997232181
 
0.3%
0.97579194041
 
0.3%
1.1334219741
 
0.3%
1.0062168541
 
0.3%
1.4596594681
 
0.3%
1.4698294611
 
0.3%
1.1552511261
 
0.3%
1.0576655221
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-30.191741751
0.3%
-20.475397561
0.3%
-2.4598805391
0.3%
-2.3427299031
0.3%
-2.060616721
0.3%
ValueCountFrequency (%)
56.798453661
0.3%
8.1018976411
0.3%
5.2307696541
0.3%
5.1823956221
0.3%
2.9926853071
0.3%

ratio_MACDh_12_26_9
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.993579996
Minimum-23.11943278
Maximum10.69510419
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:05.668977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-23.11943278
5-th percentile-0.7738310761
Q10.6111542766
median0.9417355324
Q31.300544748
95-th percentile3.462429258
Maximum10.69510419
Range33.81453697
Interquartile range (IQR)0.6893904711

Descriptive statistics

Standard deviation2.150552776
Coefficient of variation (CV)2.164448544
Kurtosis47.78041744
Mean0.993579996
Median Absolute Deviation (MAD)0.3398639006
Skewness-3.607369284
Sum384.5154585
Variance4.624877243
MonotocityNot monotonic
2021-04-09T21:52:05.964823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85901219511
 
0.3%
0.75588909151
 
0.3%
0.41550514971
 
0.3%
1.0086028691
 
0.3%
9.1698675671
 
0.3%
0.83316113271
 
0.3%
1.544381821
 
0.3%
-0.71745669981
 
0.3%
1.2184667491
 
0.3%
0.43352488341
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-23.119432781
0.3%
-12.007712821
0.3%
-8.2645344371
0.3%
-6.3803130331
0.3%
-4.3497912861
0.3%
ValueCountFrequency (%)
10.695104191
0.3%
10.610850271
0.3%
9.1698675671
0.3%
8.8017702731
0.3%
8.6111671891
0.3%

obv_pct_delta
Real number (ℝ)

UNIQUE

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04983393888
Minimum-112.3494949
Maximum61.27477134
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2021-04-09T21:52:06.243844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-112.3494949
5-th percentile-1.772188661
Q1-0.2115221872
median-0.008432564665
Q30.1285253085
95-th percentile1.334867226
Maximum61.27477134
Range173.6242663
Interquartile range (IQR)0.3400474957

Descriptive statistics

Standard deviation7.116779313
Coefficient of variation (CV)-142.8098897
Kurtosis178.5678211
Mean-0.04983393888
Median Absolute Deviation (MAD)0.1633393061
Skewness-7.937714215
Sum-19.28573435
Variance50.64854779
MonotocityNot monotonic
2021-04-09T21:52:06.592322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15851576471
 
0.3%
0.00021185768221
 
0.3%
0.015657055031
 
0.3%
0.075592536821
 
0.3%
-0.071357235711
 
0.3%
1.6884603211
 
0.3%
-0.35419819921
 
0.3%
-0.68800706841
 
0.3%
-0.0065513005951
 
0.3%
0.041584614471
 
0.3%
Other values (377)377
97.4%
ValueCountFrequency (%)
-112.34949491
0.3%
-14.812006821
0.3%
-7.8799384981
0.3%
-7.1820322251
0.3%
-6.2875588641
0.3%
ValueCountFrequency (%)
61.274771341
0.3%
43.844161581
0.3%
13.152586691
0.3%
12.241948851
0.3%
12.202830191
0.3%

LRm_3_pct_delta
Real number (ℝ)

Distinct374
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8217232778
Minimum-66.99847416
Maximum28.00076296
Zeros2
Zeros (%)0.5%
Memory size3.1 KiB
2021-04-09T21:52:06.935349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-66.99847416
5-th percentile-5.371590123
Q1-1.444443267
median-0.5625006519
Q30.2500009934
95-th percentile5.344445563
Maximum28.00076296
Range94.99923711
Interquartile range (IQR)1.69444426

Descriptive statistics

Standard deviation7.293506562
Coefficient of variation (CV)-8.875867045
Kurtosis34.48842481
Mean-0.8217232778
Median Absolute Deviation (MAD)0.8288043269
Skewness-3.754563903
Sum-318.0069085
Variance53.19523797
MonotocityNot monotonic
2021-04-09T21:52:07.240372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.53
 
0.8%
-13
 
0.8%
02
 
0.5%
-4.249994042
 
0.5%
11.500011922
 
0.5%
21.000524532
 
0.5%
-22
 
0.5%
-1.923075232
 
0.5%
-2.4999990072
 
0.5%
0.52
 
0.5%
Other values (364)365
94.3%
ValueCountFrequency (%)
-66.998474161
0.3%
-53.001287491
0.3%
-49.998855621
0.3%
-46.50005961
0.3%
-22.499475491
0.3%
ValueCountFrequency (%)
28.000762961
0.3%
27.000023841
0.3%
25.000222531
0.3%
21.999475491
0.3%
21.000524532
0.5%

tr_pct_delta
Real number (ℝ)

ZEROS

Distinct374
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1486994907
Minimum-0.8603989252
Maximum6.977280313
Zeros8
Zeros (%)2.1%
Memory size3.1 KiB
2021-04-09T21:52:07.550395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8603989252
5-th percentile-0.5552879595
Q1-0.2697499742
median-0.02702636524
Q30.3239267292
95-th percentile1.294617552
Maximum6.977280313
Range7.837679239
Interquartile range (IQR)0.5936767034

Descriptive statistics

Standard deviation0.7772952134
Coefficient of variation (CV)5.22728901
Kurtosis28.04529821
Mean0.1486994907
Median Absolute Deviation (MAD)0.2929733296
Skewness4.210400729
Sum57.54670289
Variance0.6041878488
MonotocityNot monotonic
2021-04-09T21:52:07.844417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
2.1%
0.1428581162
 
0.5%
-0.085713546132
 
0.5%
0.5000017032
 
0.5%
0.15999984742
 
0.5%
-0.15384587172
 
0.5%
0.030303730892
 
0.5%
-0.095237987111
 
0.3%
-0.37777810741
 
0.3%
-0.34090817961
 
0.3%
Other values (364)364
94.1%
ValueCountFrequency (%)
-0.86039892521
0.3%
-0.80188719561
0.3%
-0.65178637191
0.3%
-0.64999988081
0.3%
-0.63636359261
0.3%
ValueCountFrequency (%)
6.9772803131
0.3%
6.1764661331
0.3%
4.8235282571
0.3%
3.5833392941
0.3%
2.6129050621
0.3%

M_ovr_S
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
209 
0
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
1209
54.0%
0178
46.0%
2021-04-09T21:52:08.556473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-09T21:52:08.782495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1209
54.0%
0178
46.0%

Most occurring characters

ValueCountFrequency (%)
1209
54.0%
0178
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number387
100.0%

Most frequent character per category

ValueCountFrequency (%)
1209
54.0%
0178
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common387
100.0%

Most frequent character per script

ValueCountFrequency (%)
1209
54.0%
0178
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387
100.0%

Most frequent character per block

ValueCountFrequency (%)
1209
54.0%
0178
46.0%

PL
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1.0
200 
0.0
187 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1161
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0200
51.7%
0.0187
48.3%
2021-04-09T21:52:09.310529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-09T21:52:09.529546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0200
51.7%
0.0187
48.3%

Most occurring characters

ValueCountFrequency (%)
0574
49.4%
.387
33.3%
1200
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number774
66.7%
Other Punctuation387
33.3%

Most frequent character per category

ValueCountFrequency (%)
0574
74.2%
1200
 
25.8%
ValueCountFrequency (%)
.387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1161
100.0%

Most frequent character per script

ValueCountFrequency (%)
0574
49.4%
.387
33.3%
1200
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161
100.0%

Most frequent character per block

ValueCountFrequency (%)
0574
49.4%
.387
33.3%
1200
 
17.2%

Interactions

2021-04-09T21:51:22.234979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:22.521235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:22.755255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:23.002275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:23.328297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:23.580320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:23.846336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:24.083354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:24.322373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:24.568396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:24.808411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:25.058429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:25.318451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:25.576468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:25.886491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:26.157515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:26.468540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:26.780560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:27.040582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:27.300603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:27.563624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:27.834644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:28.099664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:28.332680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:28.564699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:28.808716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:29.048736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:29.302758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:29.549774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:29.970804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:30.262828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:30.529850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:30.777869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:31.034887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:31.290908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:31.545927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:31.792943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:32.049965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:32.304986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:32.560001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:32.805022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:33.055041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:33.302060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:33.547077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:33.805098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:34.053117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:34.311136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:34.541153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:34.797175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:35.055192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:35.312217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:35.561232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:35.812251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:36.206285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:36.447298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:36.696319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:36.941340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:37.206357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:37.453375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:37.706396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:37.957414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:38.211436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:38.452451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:38.700470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:38.957491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:39.206509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:39.463530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:39.713547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:39.967567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:40.209587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:40.461605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:40.710624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:40.967645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:41.211662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:41.461682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:41.708700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:41.959720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:42.363750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:42.590767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:42.831787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:43.081805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:43.358831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:43.611845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:43.867866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:44.105883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:44.339901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:44.575920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:44.805937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:45.057959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:45.309975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:45.554994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:45.787011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:46.039032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:46.294051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:46.550074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:46.829094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:47.096115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:47.359131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:47.618151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:47.880174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:48.130193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:48.384209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:48.787242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:49.054263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:49.324513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:49.612535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:49.905558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:50.170572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:50.425595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:50.670612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:51.012638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:51.311792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:51.570810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:51.807830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:52.165858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:52.611890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:52.900914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:53.175936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:53.434951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:53.690971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:53.953992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:54.200011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:54.448030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:54.706048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:54.950069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:55.210088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:55.474111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:55.913142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:56.186163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:56.438185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:56.689204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-09T21:51:56.945220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-04-09T21:52:09.774565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-09T21:52:10.219779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-09T21:52:10.623810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-09T21:52:11.040843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-09T21:52:11.418869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-09T21:51:57.324248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-09T21:51:57.744281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

closeRSI_14INC_2ROC_2PSL_3CDL_DOJI_3_0.1TRUERANGE_1Z_30ratio_M50M180ratio_M5M20ratio_M20M50ratio_MACDh_12_26_9obv_pct_deltaLRm_3_pct_deltatr_pct_deltaM_ovr_SPL
011.8947.4300930-0.99916633.3333331.00.320.1315150.9810460.2964120.748270-0.1568960.021196-0.586207-0.30434901.0
15.0966.848645118.64802366.6666670.01.064.1535550.975730-124.6648040.7147312.2851260.527121-6.7142932.53333111.0
24.8259.1631580-4.9309660.0000000.00.371.6795770.9699111.5891240.2832160.985511-0.54234411.5000120.12121210.0
37.8054.79173616.267030100.0000000.00.440.4522931.0477341.0276030.9554880.7564590.1507672.2857070.76000001.0
44.5428.6721720-7.91074833.3333331.00.15-2.3639440.9720941.3952102.2072071.538339-1.060547-0.204082-0.42307701.0
55.2840.7613630-1.49253666.6666670.00.16-1.6587010.9765370.9787560.3885060.886218-0.069720-1.8888910.00000000.0
65.5145.0059140-3.16344266.6666670.00.37-0.9077260.9708880.5990151.5100790.395303-0.033605-1.5142850.08823600.0
710.4446.1674080-3.60111133.3333330.00.63-0.4434041.0184511.6824270.8861701.171801-0.018824-0.5411760.53658301.0
86.5853.5481290-7.45429333.3333330.00.560.7235930.9517860.8055560.9492710.154373-0.044350-0.6214280.00000011.0
97.3270.650286123.025217100.0000000.01.223.4548710.956750-3.6060600.994154-1.65605161.2747712.7027056.17646611.0

Last rows

closeRSI_14INC_2ROC_2PSL_3CDL_DOJI_3_0.1TRUERANGE_1Z_30ratio_M50M180ratio_M5M20ratio_M20M50ratio_MACDh_12_26_9obv_pct_deltaLRm_3_pct_deltatr_pct_deltaM_ovr_SPL
3776.5555.3275120-5.2098360.0000000.00.460.5066440.2746880.4968471.0053892.947136-0.2492290.5000001.30000200.0
37810.6848.2774740-2.01834233.3333330.00.37-0.6324901.0107850.6969700.9002420.798640-0.0025030.999983-0.27451000.0
3795.7449.56749411.95381333.3333330.00.23-0.1885520.9690600.5266671.0893650.8974640.028058-1.578947-0.08000211.0
38012.4536.7770710-5.7532190.0000000.00.42-2.0889510.8368371.8864180.9087631.606067-0.8671370.5510220.68000000.0
3814.5350.34431517.60095466.6666670.00.371.7482991.0422880.9218750.8395580.933896-0.339043-3.909089-0.05128111.0
3823.8939.9768000-8.4705860.0000000.00.35-0.1321461.0470311.1085920.8843670.8563220.5445971.2500010.20689610.0
38311.9441.7719090-2.29133133.3333330.00.49-0.9373171.002420-0.3228990.617872-4.349791-0.001336-0.641025-0.48958400.0
38413.1053.50803510.46012666.6666670.00.250.9846011.0023690.6848200.6657070.728415-0.071357-0.599999-0.28571510.0
3856.2650.8361680-18.2767580.0000001.00.920.2900190.9507551.1488490.9527970.342021-0.5859060.060606-0.01075310.0
38611.7941.0784460-3.36065566.6666670.00.71-1.1223021.0102311.9336740.2289481.2721670.042198-2.2424200.26785600.0